{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames, GLM, Plots, Missings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These data numbers, were each collected via a simple Google search on Jan 17, 2024 with the search string \"amount of data in the world in YYYY\", where YYYY is the year. We only collected results for which Google displayed a summarizing string. The first two years, 2006, and 2007 were in etabytes, and the later in zetabytes (ZB). We converted everything to ZB. Some years presented the stored data and others the created data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>7×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">year</th><th style = \"text-align: left;\">created_data</th><th style = \"text-align: left;\">stored_data</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Union{Missing, Float64}\" style = \"text-align: left;\">Float64?</th><th title = \"Union{Missing, Float64}\" style = \"text-align: left;\">Float64?</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">0.16</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">7</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: right;\">0.29</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">1.2</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">12</td><td style = \"font-style: italic; text-align: right;\">missing</td><td style = \"text-align: right;\">2.8</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">18</td><td style = \"text-align: right;\">33.0</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">21</td><td style = \"text-align: right;\">79.0</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">22</td><td style = \"text-align: right;\">97.0</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& year & created\\_data & stored\\_data\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64? & Float64?\\\\\n",
       "\t\\hline\n",
       "\t1 & 6 & 0.16 & \\emph{missing} \\\\\n",
       "\t2 & 7 & \\emph{missing} & 0.29 \\\\\n",
       "\t3 & 10 & 1.2 & \\emph{missing} \\\\\n",
       "\t4 & 12 & \\emph{missing} & 2.8 \\\\\n",
       "\t5 & 18 & 33.0 & \\emph{missing} \\\\\n",
       "\t6 & 21 & 79.0 & \\emph{missing} \\\\\n",
       "\t7 & 22 & 97.0 & \\emph{missing} \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m7×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m year  \u001b[0m\u001b[1m created_data \u001b[0m\u001b[1m stored_data \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Float64?     \u001b[0m\u001b[90m Float64?    \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │     6          0.16 \u001b[90m  missing    \u001b[0m\n",
       "   2 │     7 \u001b[90m   missing    \u001b[0m        0.29\n",
       "   3 │    10          1.2  \u001b[90m  missing    \u001b[0m\n",
       "   4 │    12 \u001b[90m   missing    \u001b[0m        2.8\n",
       "   5 │    18         33.0  \u001b[90m  missing    \u001b[0m\n",
       "   6 │    21         79.0  \u001b[90m  missing    \u001b[0m\n",
       "   7 │    22         97.0  \u001b[90m  missing    \u001b[0m"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame(year = [2006, 2007, 2010, 2012, 2018, 2021, 2022],\n",
    "                 created_data = [0.16, missing, 1.2, missing, 33, 79, 97 ],\n",
    "                 stored_data = [missing, 0.29, missing, 2.8, missing, missing, missing])\n",
    "data.year = data.year .- 2000; #change year to be since 2000 - needed for fitting exponential model\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model, $\\log(y) = \\beta_0 + \\beta_1 x$, \n",
    "\n",
    "or $y = e^{\\beta_0} e^{\\beta_1 x} = K e^{\\alpha x}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}\n",
       "\n",
       ":(log(created_data)) ~ 1 + year\n",
       "\n",
       "Coefficients:\n",
       "─────────────────────────────────────────────────────────────────────────\n",
       "                Coef.  Std. Error       t  Pr(>|t|)  Lower 95%  Upper 95%\n",
       "─────────────────────────────────────────────────────────────────────────\n",
       "(Intercept)  -4.02624   0.323964   -12.43    0.0011  -5.05724   -2.99525\n",
       "year          0.40158   0.0194651   20.63    0.0002   0.339633   0.463526\n",
       "─────────────────────────────────────────────────────────────────────────"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_created = fit(LinearModel, @formula(log(created_data) ~ year), data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.01784121566761864, 0.40157956548341944)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "β0, β1 = coef(model_created);\n",
    "K, α = exp(β0), β1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stored data at time t:\n",
    "\n",
    "$$\n",
    "\\int_{-\\infty}^t K e^{\\alpha u} du = \\frac{K}{\\alpha} e^{\\alpha t}\n",
    "$$\n",
    "\n",
    "It is just predicted at time $t$ divided by $\\alpha$.\n",
    "\n",
    "So can use these stored observations multiplied by the initial alpha estimate as other observations.\n",
    "\n",
    "This is like a two stage analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div style = \"float: left;\"><span>7×3 DataFrame</span></div><div style = \"clear: both;\"></div></div><div class = \"data-frame\" style = \"overflow-x: scroll;\"><table class = \"data-frame\" style = \"margin-bottom: 6px;\"><thead><tr class = \"header\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">Row</th><th style = \"text-align: left;\">year</th><th style = \"text-align: left;\">created_data</th><th style = \"text-align: left;\">stored_data</th></tr><tr class = \"subheader headerLastRow\"><th class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\"></th><th title = \"Int64\" style = \"text-align: left;\">Int64</th><th title = \"Union{Missing, Float64}\" style = \"text-align: left;\">Float64?</th><th title = \"Union{Missing, Float64}\" style = \"text-align: left;\">Float64?</th></tr></thead><tbody><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">1</td><td style = \"text-align: right;\">6</td><td style = \"text-align: right;\">0.16</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">2</td><td style = \"text-align: right;\">7</td><td style = \"text-align: right;\">0.72</td><td style = \"text-align: right;\">0.29</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">3</td><td style = \"text-align: right;\">10</td><td style = \"text-align: right;\">1.2</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">4</td><td style = \"text-align: right;\">12</td><td style = \"text-align: right;\">6.97</td><td style = \"text-align: right;\">2.8</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">5</td><td style = \"text-align: right;\">18</td><td style = \"text-align: right;\">33.0</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">6</td><td style = \"text-align: right;\">21</td><td style = \"text-align: right;\">79.0</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr><tr><td class = \"rowNumber\" style = \"font-weight: bold; text-align: right;\">7</td><td style = \"text-align: right;\">22</td><td style = \"text-align: right;\">97.0</td><td style = \"font-style: italic; text-align: right;\">missing</td></tr></tbody></table></div>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccc}\n",
       "\t& year & created\\_data & stored\\_data\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Float64? & Float64?\\\\\n",
       "\t\\hline\n",
       "\t1 & 6 & 0.16 & \\emph{missing} \\\\\n",
       "\t2 & 7 & 0.72 & 0.29 \\\\\n",
       "\t3 & 10 & 1.2 & \\emph{missing} \\\\\n",
       "\t4 & 12 & 6.97 & 2.8 \\\\\n",
       "\t5 & 18 & 33.0 & \\emph{missing} \\\\\n",
       "\t6 & 21 & 79.0 & \\emph{missing} \\\\\n",
       "\t7 & 22 & 97.0 & \\emph{missing} \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m7×3 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m year  \u001b[0m\u001b[1m created_data \u001b[0m\u001b[1m stored_data \u001b[0m\n",
       "     │\u001b[90m Int64 \u001b[0m\u001b[90m Float64?     \u001b[0m\u001b[90m Float64?    \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │     6          0.16 \u001b[90m  missing    \u001b[0m\n",
       "   2 │     7          0.72         0.29\n",
       "   3 │    10          1.2  \u001b[90m  missing    \u001b[0m\n",
       "   4 │    12          6.97         2.8\n",
       "   5 │    18         33.0  \u001b[90m  missing    \u001b[0m\n",
       "   6 │    21         79.0  \u001b[90m  missing    \u001b[0m\n",
       "   7 │    22         97.0  \u001b[90m  missing    \u001b[0m"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2,:created_data] = round(data[2,:stored_data]/α, digits = 2)\n",
    "data[4,:created_data] = round(data[4,:stored_data]/α, digits = 2)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now fit again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}\n",
       "\n",
       ":(log(created_data)) ~ 1 + year\n",
       "\n",
       "Coefficients:\n",
       "─────────────────────────────────────────────────────────────────────────\n",
       "                 Coef.  Std. Error      t  Pr(>|t|)  Lower 95%  Upper 95%\n",
       "─────────────────────────────────────────────────────────────────────────\n",
       "(Intercept)  -3.31981    0.508708   -6.53    0.0013  -4.62749   -2.01214\n",
       "year          0.371273   0.0338816  10.96    0.0001   0.284178   0.458368\n",
       "─────────────────────────────────────────────────────────────────────────"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_created = fit(LinearModel, @formula(log(created_data) ~ year), data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.03615957973444979, 0.3712730333820715)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "β0, β1 = coef(model_created);\n",
    "K, α = exp(β0), β1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.48631333906767127, 3.1126144576944736)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a bit more: See the ratio of created data to stored data. We have stored data web search results in 2007, and 2012. Let's see the predictions for these years:\n",
    "pred_created(2007), pred_created(2012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.6769425485092113, 1.1116480206051693)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_created(2007)/0.29, pred_created(2012)/2.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pred_stored (generic function with 1 method)"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_created(year) = K*exp(α*(year-2000))\n",
    "pred_stored(year) = (1/3) * pred_created(year)/α # Assume a third of the data is being kept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(2005:2025, pred_created.(2005:2025), lw = 1, label = \"Fit of created\", c = :black)\n",
    "scatter!(dropmissing(data,:created_data).year .+ 2000, \n",
    "        dropmissing(data,:created_data).created_data,\n",
    "        label = \"Yearly estimate created\", c = :black,\n",
    "        xticks = 2000:5:2025, xlim = (2005, 2026), ylim = (0,150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = 2020:2030\n",
    "yota_world = pred_stored.(years)/1024\n",
    "bar(years,yota_world,xticks = years,label = :none,\n",
    "    xlabel = \"Year\", ylabel = \"Data volume in yotabytes\",ylim = (0,3.1))"
   ]
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  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"/Users/uqjnazar/Dropbox/MathEngDeepLearningBook/LaTeXBook/figures/chapter_1_figures/years_data_zb.pdf\""
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savefig(\"/Users/uqjnazar/Dropbox/MathEngDeepLearningBook/LaTeXBook/figures/chapter_1_figures/years_data_zb.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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